Abstract

Current explanatory concepts suggest seizures emerge from ongoing dynamics of brain networks. It is unclear how brain network properties determine focal or generalised seizure onset, or how network properties can be described in a clinically-useful manner. Understanding network properties would cast light on seizure-generating mechanisms and allow to quantify to which extent a seizure is focal or generalised. Functional brain networks were estimated in segments of scalp-EEG without interictal discharges (68 people with epilepsy, 38 controls). Simplified brain dynamics were simulated using a computer model. We introduce: Critical Coupling (Cc), the ability of a network to generate seizures; Onset Index (OI), the tendency of a region to generate seizures; and Participation Index (PI), the tendency of a region to become involved in seizures. Cc was lower in both patient groups compared with controls. OI and PI were more variable in focal-onset than generalised-onset cases. In focal cases, the regions with highest OI and PI corresponded to the side of seizure onset. Properties of interictal functional networks from scalp EEG can be estimated using a computer model and used to predict seizure likelihood and onset patterns. This may offer potential to enhance diagnosis through quantification of seizure type using inter-ictal recordings.

Details

Title
Dynamic network properties of the interictal brain determine whether seizures appear focal or generalised
Author
Wessel, Woldman 1   VIAFID ORCID Logo  ; Schmidt, Helmut 2 ; Abela Eugenio 3 ; Chowdhury, Fahmida A 4 ; Pawley, Adam D 3 ; Jewell, Sharon 3 ; Richardson, Mark P 5 ; Terry, John R 1 

 University of Birmingham, Centre for Systems Modelling and Quantitative Biomedicine, Birmingham, United Kingdom (GRID:grid.6572.6) (ISNI:0000 0004 1936 7486); University of Exeter, Centre for Biomedical Modelling and Analysis, Exeter, United Kingdom (GRID:grid.8391.3) (ISNI:0000 0004 1936 8024); University of Exeter, EPSRC Centre for Predictive Modelling in Healthcare, Exeter, United Kingdom (GRID:grid.8391.3) (ISNI:0000 0004 1936 8024) 
 Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany (GRID:grid.419524.f) (ISNI:0000 0001 0041 5028) 
 Psychology and Neuroscience, King’s College London, Institute of Psychiatry, London, United Kingdom (GRID:grid.13097.3c) (ISNI:0000 0001 2322 6764); King’s College London, Department of Basic and Clinical Neurosciences, London, United Kingdom (GRID:grid.13097.3c) (ISNI:0000 0001 2322 6764) 
 NIHR University College London Hospitals Biomedical Research Centre, UCL Institute of Neurology, Queen Square, London, United Kingdom (GRID:grid.83440.3b) (ISNI:0000000121901201) 
 University of Birmingham, Centre for Systems Modelling and Quantitative Biomedicine, Birmingham, United Kingdom (GRID:grid.6572.6) (ISNI:0000 0004 1936 7486); Psychology and Neuroscience, King’s College London, Institute of Psychiatry, London, United Kingdom (GRID:grid.13097.3c) (ISNI:0000 0001 2322 6764); King’s College London, Department of Basic and Clinical Neurosciences, London, United Kingdom (GRID:grid.13097.3c) (ISNI:0000 0001 2322 6764) 
Publication year
2020
Publication date
2020
Publisher
Nature Publishing Group
e-ISSN
20452322
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2395250052
Copyright
© The Author(s) 2020. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.